TRAVEL TIME DATA COLLECTION AND SPATIAL INFORMATION TECHNOLOGIES FOR RELIABLE TRANSPORTATION SYSTEMS PLANNING

Transcription

1 TRAVEL TIME DATA COLLECTION AND SPATIAL INFORMATION TECHNOLOGIES FOR RELIABLE TRANSPORTATION SYSTEMS PLANNING Srinivas S. Pulugurtha, Ph.D., P.E. Venkata R. Duddu, Ph.D., E.I. The University of North Carolina at Charlotte (UNC Charlotte) TRB Sensing Technologies for Transportation Applications Workshop #148 January 12, 2014

2 Research Team Dr. Edd Hauser - Director, Ctr. for Transp. Policy Studies Dr. Xiaoyu Wang - Research Assistant Prof., Charlotte Vis. Ctr. Graduate Students Rahul Pinnamaneni R. M. Zahid Reza Sai Venkata Nallamalli Vinay Thokala Vishnu Payyavula Md. Shah Imran Ravi Kiran Puvvala Pooya Najaf

3 Key Research Tasks Compare and evaluate travel time data from different technologies / non-connected devices Develop data tools and query applications Assess transportation systems reliability Compute reliability measures Link- or corridor-level? Evaluate correlation between different reliability measures Identify thresholds and level-of-service (LOS) categories Model the effect of incidents on reliability Develop DSS tools using visualization techniques

4 Comparative Evaluation of Travel Time from Different Technologies and Sources

5 Travel Time Data Collection & Technologies / Sources Study area: Charlotte, North Carolina Technologies / sources: Manual & GPS Bluetooth devices Private sources such as INRIX Automatic Vehicle Location (AVL) units on buses

6 Data Collection Manual, GPS & Bluetooth data collection 6 corridors Peak and off-peak hours on two consecutive weekdays for each corridor Study Corridors for Data Collection Route Number Route Name Type No. of Lanes AADT Bus Availability Weekdays Weekends Speed Limit (mph) 11 North Tryon Major Arterial 3 25,000-30,000 Yes Yes South Blvd Arterial 2 20,000-25,000 Yes Yes Providence Road Arterial 2 30,000-40,000 Yes Yes Sharon Road Local 2 14,000-20,000 Yes No Graham Street Rd Arterial 2 14,000-20,000 Yes Yes 45 I-85 Interstate 85 Freeway 4 30,000-60,000 No No 70

7 Data Collection (Cont.) Run 1 - End Point Time: 07:15 AM Run 1- Start Point Time: 06:55 AM

8 Study Corridors for Manual, GPS & Bluetooth Data Collection

9 Manual data Preparing travel time worksheet Data Processing Recording elapsed time data between signalized intersections and busstop locations Recording delay at signals GPS data GPS device installed in a test vehicle Travel time data from GPS was directly recorded into a Laptop PC-Travel Software was used to process the GPS data Bluetooth data Collection of raw data from USB flash drives connected to the devices Data filtering techniques were applied based on minimum and maximum travel speeds Travel time based on minimum and maximum possible speeds

10 Comparison of Travel Time by Travel Run during Off-peak (Mid-Day) and Peak (Evening) Periods along South Blvd (Top) & I-85 (Bottom) ID Manual (Sec) GPS (%) INRIX (%) Bluetooth (%) Manual (Sec) GPS (%) INRIX (%) Bluetooth (%) Manual (Sec) GPS (%) INRIX (%) Bluetooth (%) 5/29/201 3 Run 1 (Time) 11:15 AM Run 2 (Time) 11:49 AM Run 3 (Time) 12:17 PM /29/201 3 Run 1 (Time) 4:46:50 PM Run 2 (Time) 5:28:00 PM Run 3 (Time) 6:20:10 PM ID Manual (Sec) GPS (%) INRIX (%) Bluetooth (%) Manual (Sec) GPS (%) INRIX (%) Bluetooth (%) Manual (Sec) GPS (%) INRIX (%) Bluetooth (%) 6/25/201 3 Run 1 (Time) 11:03:27 AM Run 2 (Time) 11:27:57 AM Run 3 (Time) 11:51:30 AM /25/201 3 Run 1 (Time) 4:07:20 PM Run 2 (Time) 4:32:50 PM Run 3 (Time) 5:13:08 PM

11 Sample Size by Time-of-the-day

12 Effect of Sample Size and Link-length / Spacing on Data Quality Timeof-theday Mid- Day PM South Blvd Inbound Direction Link1 (1.3 miles) Link 2 (1.3 miles) Link 3 (1.9 miles) Link 4 (0.8 miles) Run Sample Percent Sample Percent Sample Percent Sample Percent Size Error Size Error Size Error Size Error

13 Relation between Bluetooth Detector Spacing and % Difference Pearson correlation between spacing and % difference is

14 Percentage Difference in Travel Time for Different Segments

15 Percentage Difference in Travel Time by Data Collection Period for All the Runs on Selected Arterial Streets

16 Comparison of Technologies Key Findings Ability (sample size) to detect differs for freeways and arterial streets More noise / disturbances lowering detection rate Detection rate varies by time-of-the-day f(traffic volume)? Weather & environmental conditions? Travel time data from both Bluetooth detectors and INRIX are reasonably close to manually captured travel time data along the freeway segment than when compared to arterials segments For arterial streets, travel times from INRIX are more promising when compared to the travel times from the Bluetooth detectors Role of network characteristics?

17 Assessing Transportation System Reliability

18 Data Collection (Cont.) Inrix data Over 200 routes Data downloaded for years 2008 to 2012

19 Data Processing & Performance Measures AVL & Inrix data Data processing and mining - performed using Microsoft SQL Server Data tools & query applications were developed to compute: Minimum Average Maximum Median 85 th Percentile 95 th Percentile (Planning Time PT) Factors considered Time-of-day & day-of-week For each run Computation of reliability measures

20 Measures of Reliability Index Equation Index Equation NCHRP Definition SD of travel time λ Skew AASHTO Definition and TranSystems Definition Probability on-time performance Buffer Time (BT) Variability TT 85 -TT 15 Buffer Time Index (BTI) First worst travel times over a month Second worst travel times over a month Planning Time (PT) Planning Time Index (PTI) Travel Time Variability (TTV) Variability TT 80 -TT 20 Variability TT 70 -TT 30 Acceptable Travel Time Variation Index Desired Travel Time Reduction Index Travel Time Index (TTI) Frequency of Congestion P(T avg +ATTV) P(Tave-DTTR) Percent of days/periods that are congested

21 Link-level Travel Time and Reliability Measures North Tryon St Corridor during Weekdays Off-peak Hours (10:00 PM - 11:00 PM) Peak Hours (8:00 AM - 9:00 AM) Link Travel Time (Minutes) Travel Time Percentile BTI BTI Travel Time (Minutes) Travel Time Percentile BTI Min Max Avg (85) (95) Min Max Avg (85) BTI (95)

22 Reliability During Weekday Peak (Left) & Off-peak (Right) Hours 27

23 Reliability During Weekend Peak (Left) & Off-peak (Right) Hours 28

24 Correlation Matrix For Travel Times and Reliability Indices For Weekday (All Day)

25 Correlation Matrix For Travel Times and Reliability Indices For Weekday (All Day) (Cont.)

26 Role of Reliability Measures Performance Measure Role Reports a nominal level of congestion as opposed to providing any Avg. Travel Time (TT) information on the variation of travel rates. Helps in evaluating unique reliability measures through correlation. TT -10 th Percentile Evaluate variance in travel times TT - 15 th Percentile Evaluate variance in Travel Times TT - 50 th Percentile Evaluate Skewness TT - 85 th Percentile These are upper percentiles of travel time distributions and can be TT - 90 th Percentile used as performance indicators. Can be used for before-and-after TT - 95 th Percentile (PT) studies for comparison. TTV - 90 (TT90-TT10) These measures indicate variability in travel times or TTV - 85 (TT85-TT15) TTV - 95 (TT95-TT15) BT BTI PTI λ Skew λ Var TTI unpredictability of travel times from the users viewpoint. Can be used for before-and-after studies for comparison. BTI and PTI help track reliability over time and evaluate the condition of the facility. All these measures can be used for ranking and prioritization by agencies. These measures can also be used to compare the performance of one segment with another. * Performance measures in the bold are not correlated with the average travel times

27 Assessing Reliability Key Findings Identification of specific locations for improvements will be difficult if corridor-level reliability measures are computed and used instead of link-level reliability measures Using corridor-level measures for prioritization and ranking may also lead to unnecessary and additional expenditures Use of 85 th or 95 th percentile travel times to compute reliability and assess performance should depend on user s acceptance levels in the region Percent of links or lane miles with poor reliability scores by time-of-the-day could be used for assessment of transportation network performance (area-level)

28 Temporal Variation in Travel Time over Space - Crash Scenario 33

29 34

30 Acknowledgements This presentation is based on information collected and research performed for a research project funded by the United States Department of Transportation Research and Innovative Technology Administration (USDOT/RITA) under Cooperative Agreement Number RITARS-12-H-UNCC. USDOT/RITA Program Manager: Mr. Caesar Singh

31 Disclaimer Views, opinions, findings, and conclusions reflected in this presentation are the responsibility of the authors only and do not represent the official policy or position of the USDOT/RITA, or any State, or the University of North Carolina at Charlotte or other entity. The authors are responsible for the facts and the accuracy of the data presented herein. This presentation does not constitute a standard, specification, or regulation.